Clustering Mobile Network Data with Decorrelating Adversarial Nets

Deep learning plays a crucial role in enabling cognitive automation for the mobile networks of the future. Deep clustering – a subset of deep learning – is a valuable tool for many network automation use cases. Unfortunately, most state-of-the-art clustering algorithms target image datasets, which makes them hard to apply to mobile network automation due to their highly tuned nature and assumptions about the data. In this paper, we propose a new algorithm, Decorrelating Adversarial Nets for Clustering-friendly Encoding (DANCE), intended to be a reliable deep clustering method for mobile network automation use cases. DANCE uses a reconstructive clustering approach, separating clustering-relevant from clustering-irrelevant features in a latent representation. This separation removes unnecessary information from the clustering, increasing consistency and peak performance. We comprehensively evaluate DANCE and other select state-of-the-art deep clustering algorithms, and show that DANCE outperforms these algorithms by a significant margin in a mobile user behavior clustering task based on data gained from a simulated scenario.